Contextual Model Selection for Computer Vision
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Solution Overview
Problem
Current computer vision systems face challenges in selecting the most relevant models and activating corresponding actions based on contextual information, leading to user burden and inefficiency as the number of available models increases.
Innovation Solution
The system retrieves contextual information from images, devices, or user contexts to identify and activate appropriate models and layers, using deep learning and traditional machine learning techniques for context-based model selection and layer activation, recommending actions and providing additional information through icons associated with applications or services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the number of available models increases to provide more comprehensive functionality, then the system's adaptability and versatility improve, but the device complexity and user burden increase
Solution Approach 1:
The system automatically selects and activates appropriate models based on contextual information without requiring user intervention. The model selection process is autonomous, using device sensors, user context, and image data to determine which models to load and when, thereby reducing user burden while maintaining comprehensive functionality.
Solution Approach 2:
The system dynamically adjusts the set of active models based on changing contexts. Models are selectively activated and deactivated according to current device state, user preferences, and environmental conditions, allowing the system to adapt its complexity level to match operational needs rather than maintaining all models simultaneously.
2Extent of automation
If contextual information is retrieved and processed to enable intelligent model selection, then the system's intelligence and relevance improve, but the processing time and energy consumption increase
Solution Approach 1:
Contextual information is continuously collected and processed in advance of actual model selection needs. Device state, user preferences, and environmental data are pre-processed and stored, allowing the system to quickly determine appropriate models without performing extensive analysis at the moment of selection.
Solution Approach 2:
The system uses feedback from previous model selections and user interactions to refine future selections. Performance data and user behavior patterns are analyzed to improve the accuracy and speed of automated model selection, reducing processing time through learned optimizations.
3Measurement precision
If multiple models are maintained to handle diverse recognition tasks, then the system's measurement precision and recognition accuracy improve, but the device complexity and resource requirements increase
Solution Approach 1:
The system divides the model set into distinct categories or layers based on functionality and complexity. Different models are organized into segments that can be independently selected and activated, making it easier to manage and replace individual models without affecting the entire system.
Solution Approach 2:
The system changes operational parameters such as model precision, resolution, and computational intensity based on task requirements and available resources. Less precise but faster models are used for preliminary processing, while more accurate models are activated only when needed, balancing precision requirements with system complexity.
Data Source
AI summary
A method includes retrieving, by a device, contextual information based on at least one of an image, the device, user context, or a combination thereof. At least one model is identified from multiple models based on the contextual information and at least one object recognized in an image based on at least one model. At least one icon is displayed at the device. The at least one icon being associated with at least one of an application, a service, or a combination thereof providing additional information.